U.S. patent number 8,478,480 [Application Number 11/748,714] was granted by the patent office on 2013-07-02 for vehicle evaluation using infrared data.
This patent grant is currently assigned to International Electronic Machines Corp.. The grantee listed for this patent is Robert W. Foss, Zahid F. Mian. Invention is credited to Robert W. Foss, Zahid F. Mian.
United States Patent |
8,478,480 |
Mian , et al. |
July 2, 2013 |
Vehicle evaluation using infrared data
Abstract
A solution for evaluating a vehicle using infrared data is
provided. In particular, evaluation data for the vehicle is
obtained, which includes infrared data for a plurality of sides of
the vehicle as well as vehicle identification data for
distinguishing the vehicle from another vehicle. The infrared data
is processed to enhance a set of signal features. Additional
non-infrared based data also can be obtained for evaluating the
vehicle. The evaluation data is analyzed to determine whether one
or more anomalies are present. The anomaly(ies) can be correlated
with a possible problem with a component of the vehicle. Data on
the anomaly, problem, and/or vehicle identification can be provided
for use on another system, such as a remote inspection station,
maintenance system, and/or the like.
Inventors: |
Mian; Zahid F. (Loudonville,
NY), Foss; Robert W. (Cohoes, NY) |
Applicant: |
Name |
City |
State |
Country |
Type |
Mian; Zahid F.
Foss; Robert W. |
Loudonville
Cohoes |
NY
NY |
US
US |
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Assignee: |
International Electronic Machines
Corp. (Troy, NY)
|
Family
ID: |
40253829 |
Appl.
No.: |
11/748,714 |
Filed: |
May 15, 2007 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20090018721 A1 |
Jan 15, 2009 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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60854703 |
Oct 27, 2006 |
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Current U.S.
Class: |
701/31.4; 702/1;
701/29.1; 348/148; 702/40; 701/33.8; 701/29.6; 250/332; 701/29.3;
701/33.7; 250/334; 348/163; 702/33; 250/316.1; 250/330 |
Current CPC
Class: |
G01M
17/013 (20130101); G06K 9/4604 (20130101); G07C
5/00 (20130101); G01N 21/88 (20130101); G01M
17/007 (20130101) |
Current International
Class: |
G01M
17/10 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Christiaen et al., "Evaluation of Infrared Brake Screening
Technology: Final Report," U.S. Department of Transportation, Dec.
2000, 90 pages. cited by applicant .
Laura Freedman, USPTO Office Action, U.S. Appl. No. 12/603,958,
Notification Date Mar. 12, 2012, 33 pages. cited by
applicant.
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Primary Examiner: Dager; Jonathan M
Attorney, Agent or Firm: LaBatt, LLC
Government Interests
GOVERNMENT LICENSE RIGHTS
The U.S. Government has a paid-up license in this invention and the
right in limited circumstances to require the patent owner to
license others on reasonable terms as provided for by the terms of
Contract Number MC-06-RA-01-G-00000 awarded by the Federal Motor
Carrier Safety Administration (FMCSA) of the U.S. Department of
Transportation.
Parent Case Text
REFERENCE TO PRIOR APPLICATIONS
The current application claims the benefit of U.S. Provisional
Application No. 60/854,703, entitled "Multifunctional vehicle
inspection system and device", which was filed on 27 Oct. 2006, and
which is hereby incorporated by reference.
Claims
What is claimed is:
1. A method of evaluating a vehicle, the method comprising:
obtaining evaluation data for the vehicle on a computer system
including at least one computing device, wherein the evaluation
data for the vehicle is concurrently acquired in a single pass, the
obtaining including: obtaining infrared data for a plurality of
sides of the vehicle on the computer system; in response to
obtaining the infrared data, processing the infrared data using the
computer system to enhance a set of signal features in the infrared
data and extract infrared wheel data corresponding to each of a
plurality of wheels on the vehicle from the infrared data, wherein
the processing uses at least one enhancement process selected from
a group of enhancement processes based on at least one anomaly, the
group of enhancement processes including: segmentation, fusion of
multiple infrared images in the infrared data, edge detection
within an infrared image in the infrared data, applying
thresholding to assign a pixel in the infrared data to white or
black, and feature detection of a wheel of the vehicle in the
infrared data; and obtaining vehicle identification data for
distinguishing the vehicle from another vehicle on the computer
system; automatically analyzing the processed infrared data to
determine a presence of the at least one anomaly using the computer
system, wherein the analyzing includes determining whether a wheel
of the vehicle comprises an infrared signature outside of an
expected infrared signature range using the processed infrared
data; and providing a result of the analyzing and the vehicle
identification data from the computer system for use at an
inspection station.
2. The method of claim 1, further comprising automatically
detecting a presence of the vehicle, the obtaining evaluation data
being automatically performed in response to the detected
presence.
3. The method of claim 1, the obtaining vehicle identification data
including obtaining a visible image of the vehicle.
4. The method of claim 1, the obtaining infrared data including:
acquiring a first infrared image for a first side of the vehicle;
and acquiring a second infrared image for a second side of the
vehicle.
5. The method of claim 4, the analyzing including examining a
portion of each of the first and second infrared images that
includes a wheel of the vehicle.
6. The method of claim 5, the examining including comparing a
brightness of a portion of at least one of the first or second
infrared images corresponding to a portion of the wheel with an
expected brightness.
7. The method of claim 6, the portion of the wheel comprising at
least one of: a wheel rim or a tread surface.
8. The method of claim 1, further comprising inspecting the vehicle
based on the result of the analyzing.
9. A system comprising: a computer system configured to evaluate a
vehicle, the computer system including at least one computing
device for performing a method comprising: obtaining evaluation
data for the vehicle, the obtaining including: obtaining infrared
data for a plurality of sides of the vehicle, wherein the infrared
data is acquired in a single pass; in response to obtaining the
infrared data, processing the infrared data to enhance a set of
signal features in the infrared data and extract infrared wheel
data corresponding to each of a plurality of wheels on the vehicle,
wherein the processing uses at least one enhancement process
selected from a group of enhancement processes based on at least
one anomaly, the group of enhancement processes including:
segmentation, fusion of multiple infrared images in the infrared
data, edge detection within an infrared image in the infrared data,
applying thresholding to assign a pixel in the infrared data to
white or black, and feature detection of a wheel of the vehicle in
the infrared data; and obtaining vehicle identification data for
distinguishing the vehicle from another vehicle; automatically
analyzing the processed infrared data to determine a presence of
the at least one anomaly, wherein the analyzing includes
determining whether a wheel of the vehicle comprises an infrared
signature outside of an expected infrared signature range using the
processed infrared data; and providing a result of the analyzing
and the vehicle identification data for use at an inspection
station.
10. The system of claim 9, the method further comprising:
automatically detecting a presence of the vehicle, the obtaining
evaluation data being automatically performed in response to the
detected presence.
11. The system of claim 9, the obtaining evaluation data further
including obtaining a visible image of the vehicle, the analyzing
further analyzing the visible image to determine the presence of at
least one anomaly.
12. The system of claim 9, the obtaining evaluation data further
including obtaining non-image data for the vehicle, the analyzing
further analyzing the non-image data to determine the presence of
at least one anomaly.
13. The system of claim 9, the obtaining infrared data including:
acquiring a first infrared image for a first side of the vehicle;
and acquiring a second infrared image for a second side of the
vehicle.
14. The system of claim 13, wherein the obtaining infrared data
includes a device located in a path of the vehicle.
15. The system of claim 9, the at least one anomaly including at
least one of: a brake anomaly, a bearing anomaly, or a wheel
anomaly.
16. A system comprising: a computer system configured to evaluate a
vehicle, the computer system including at least one computing
device for performing a method comprising: automatically detecting
the vehicle; obtaining evaluation data for the vehicle in response
to detecting the vehicle, wherein the evaluation data includes
infrared data corresponding to a component of the vehicle enhanced
and extracted from infrared data captured by at least one infrared
device, and wherein the infrared data is enhanced and extracted
using at least one enhancement process selected from a group of
enhancement processes based on at least one anomaly, the group of
enhancement processes including: segmentation, fusion of multiple
infrared images in the infrared data, edge detection within an
infrared image in the infrared data, applying thresholding to
assign a pixel in the infrared data to white or black, and feature
detection of the component of the vehicle in the infrared data; and
automatically analyzing the evaluation data to determine a presence
of the at least one anomaly, wherein the analyzing includes
determining whether the component of the vehicle comprises an
infrared signature outside of an expected infrared signature range
using the evaluation data; and an identification device for
obtaining vehicle identification data for distinguishing the
vehicle from another vehicle, wherein the evaluation data further
includes the vehicle identification data obtained by the
identification device.
17. The system of claim 16, the method further comprising providing
a result of the analyzing and the vehicle identification data for
use at an inspection station.
18. The system of claim 16, further comprising a system for
obtaining non-infrared data for the vehicle, wherein the evaluation
data further includes the non-infrared data.
19. The system of claim 18, the non-infrared data including at
least one of: a visible image or acoustic data.
20. The system of claim 16, further comprising: a first infrared
device on a first side of the vehicle; and a second infrared device
on a second side of the vehicle, distinct from the first side, the
at least one anomaly including at least one of: a brake anomaly, a
bearing anomaly, or a wheel anomaly.
Description
FIELD OF THE INVENTION
Aspects of the invention relate generally to vehicle evaluation,
and more particularly, to a solution for evaluating a vehicle using
infrared data.
BACKGROUND OF THE INVENTION
Vehicles, particularly commercial vehicles such as trucks, buses,
and the like, transport millions of tons of cargo and millions of
passengers to a variety of destinations worldwide. While the
overwhelming majority of these trips are uneventful, a significant
number of these trips experiences a problem due to a failure of a
component of the vehicle itself. Such a problem can cause a delay
and/or an accident, the latter of which may result in damage to the
vehicle, its cargo, injured individual(s), loss of life, and/or the
like.
To limit the unanticipated failure of a vehicle component, most
vehicles, and all commercial vehicles, are generally required to
undergo regular inspections. Further, additional inspections,
particularly of commercial vehicles, often are carried out at
random times and/or locations by members of state and/or federal
enforcement organizations (e.g., state police, Department of
Transportation, etc.). However, with a large number of vehicles
involved in a random inspection, it is extremely difficult for an
inspector to reliably select the vehicles most likely to experience
a failure. Often, this is due to the limited resources and
technologies available to the inspector and/or the implementation
of the component. For example, electric and hydraulic brakes,
unlike air brakes, cannot be readily visually evaluated by an
inspector since they have no visible moving parts.
It is well known that brakes will heat up when used to slow a
vehicle since the friction will dissipate the motion energy into
heat. When a brake is not functioning properly, excessive or
insufficient heat may be present in the braking area after the
brakes have been used. Similarly, other components of a vehicle may
show abnormal heat distribution as they approach failure. For
example, improperly functioning bearings may result in increased
friction, and therefore heat, between a wheel and an axle.
Additionally, a failing tire may have increased heat in an area due
to increased flexing and friction. A heat differential also can
indicate other significant phenomena, such as leakage of cargo
(e.g., from a tanker), leakage of exhaust, and/or the like.
Some inspection approaches use heat to determine if a vehicle brake
component must be directly tested. For example, an inspector may
place his/her hand near a vehicle's hydraulic or electric brake
area to determine if it appears abnormally warmer than the
surrounding air. However, this approach has a number of drawbacks
including variations in inspectors and environmental conditions,
variations in the amount of braking used (e.g., loaded versus
unloaded truck), slow and invasive examination, which requires the
truck to be stopped, and the like. Additionally, another approach
uses thermal, or infrared, imaging to detect a defect in a vehicle
brake component. In this approach, a human user evaluates a thermal
image as a vehicle passes an imaging system set up adjacent to a
road. However, this approach is limited in that, among other
things, it requires a specially trained individual to evaluate the
thermal images and/or operate the system, only a single side of the
vehicle is imaged, it fails to address communications with an
inspection site and/or logging data, and the like.
BRIEF SUMMARY OF THE INVENTION
Aspects of the invention provide a solution for evaluating a
vehicle using infrared data. In particular, evaluation data for the
vehicle is obtained, which includes infrared data for a plurality
of sides of the vehicle as well as vehicle identification data for
distinguishing the vehicle from another vehicle. The infrared data
is processed to enhance a set of signal features. Additional
non-infrared based data also can be obtained for evaluating the
vehicle. The evaluation data is analyzed to determine whether one
or more anomalies are present. The anomaly(ies) can be correlated
with a possible problem with a component of the vehicle. Data on
the anomaly, problem, and/or vehicle identification can be provided
for use on another system, such as a remote inspection station,
maintenance system, and/or the like.
A first aspect of the invention provides a method of evaluating a
vehicle, the method comprising: obtaining evaluation data for the
vehicle, the obtaining including: obtaining infrared data for a
plurality of sides of the vehicle; processing the infrared data to
enhance a signal feature; and obtaining vehicle identification data
for distinguishing the vehicle from another vehicle; analyzing the
infrared data to determine a presence of at least one anomaly; and
providing a result of the analyzing and the vehicle identification
data for use at a remote inspection station.
A second aspect of the invention provides a system for evaluating a
vehicle, the system comprising: a system for obtaining evaluation
data for the vehicle, the system for obtaining including: a system
for obtaining infrared data for a plurality of sides of the
vehicle; a system for processing the infrared data to enhance a
signal feature; and a system for obtaining vehicle identification
data for distinguishing the vehicle from another vehicle; a system
for analyzing the infrared data to determine a presence of at least
one anomaly; and a system for providing a result of the analyzing
and the vehicle identification data for use at a remote inspection
station.
A third aspect of the invention provides a system for evaluating a
vehicle, the system comprising: a system for automatically
detecting the vehicle; a system for obtaining evaluation data for
the vehicle, the system for obtaining evaluation data including: a
first infrared device on a first side of the vehicle; a second
infrared device on a second side of the vehicle; and an
identification device for obtaining vehicle identification data for
distinguishing the vehicle from another vehicle; and a system for
analyzing the evaluation data to determine a presence of at least
one anomaly.
Other aspects of the invention provide methods, systems, program
products, and methods of using and generating each, which include
and/or implement some or all of the actions described herein. The
illustrative aspects of the invention are designed to solve one or
more of the problems herein described and/or one or more other
problems not discussed.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
These and other features of the invention will be more readily
understood from the following detailed description of the various
aspects of the invention taken in conjunction with the accompanying
drawings that depict various embodiments of the invention.
FIG. 1 shows an illustrative environment for evaluating a vehicle
according to an embodiment.
FIG. 2 shows a more detailed view of the computer system of FIG. 1
according to an embodiment.
FIG. 3 shows an illustrative process for evaluating a vehicle
according to an embodiment.
FIGS. 4A-C show an illustrative acquisition unit according to an
embodiment.
FIGS. 5A-C show another illustrative acquisition unit according to
an embodiment.
FIG. 6 shows an illustrative configuration of acquisition units
according to an embodiment.
FIG. 7 shows an illustrative vehicle and vehicle wheel, which can
be evaluated according to an embodiment.
FIG. 8 shows an illustrative series of images of a wheel and the
resulting infrared data after processing with two illustrative
evaluation solutions according to an embodiment.
FIG. 9 shows an illustrative use of a linear array according to an
embodiment.
It is noted that the drawings are not to scale. The drawings are
intended to depict only typical aspects of the invention, and
therefore should not be considered as limiting the scope of the
invention. In the drawings, like numbering represents like elements
between the drawings.
DETAILED DESCRIPTION OF THE INVENTION
As indicated above, aspects of the invention provide a solution for
evaluating a vehicle using infrared data. In particular, evaluation
data for the vehicle is obtained, which includes infrared data for
a plurality of sides of the vehicle as well as vehicle
identification data for distinguishing the vehicle from another
vehicle. The infrared data is processed to enhance a set of signal
features. Additional non-infrared based data also can be obtained
for evaluating the vehicle. The evaluation data is analyzed to
determine whether one or more anomalies are present. The
anomaly(ies) can be correlated with a possible problem with a
component of the vehicle. Data on the anomaly, problem, and/or
vehicle identification can be provided for use on another system,
such as a remote inspection station, maintenance system, and/or the
like. As used herein, unless otherwise noted, the term "set" means
one or more (i.e., at least one) and the phrase "any solution"
means any now known or later developed solution.
Turning to the drawings, FIG. 1 shows an illustrative environment
10 for inspecting a vehicle 2 according to an embodiment. To this
extent, environment 10 includes a computer system 11, which
includes various computing devices 12A-B, 14, 16, and 18A-B that
can perform the process described herein in order to evaluate
vehicle 2. In particular, computer system 11 includes infrared
devices 12A-B, an evaluation device 14, an identification device
16, and sensing devices 18A-B. During operation, sensing devices
18A-B can detect a presence of a vehicle 2, identification device
16 can obtain identification data for vehicle 2, and each infrared
device 12A-B can obtain infrared data from a corresponding side of
vehicle 2. Devices 12A-B, 16 can provide the raw data and/or
preprocessed data for further processing on evaluation device
14.
Evaluation device 14 can perform advanced image processing on the
infrared data and/or analyze the infrared data to determine whether
one or more anomalies are present. As illustrated, computer system
11 is implemented in conjunction with an inspection system 40. To
this extent, the evaluation of the infrared data can be performed
as part of a prescreening process for vehicles 2 being inspected.
In this case, evaluation device 14 can communicate the results of
the prescreening of each vehicle 2 to inspection system 40, which
is utilized in performing the inspection. Inspection system 40 can
comprise any type of inspection system now known or later
developed. Based on the result for vehicle 2, an inspector can
adjust one or more aspects of the inspection (e.g., perform a
more/less thorough inspection of a braking system).
FIG. 2 shows a more detailed view of computer system 11 according
to an embodiment. In general, computer system 11 includes various
subsystems, which can be implemented on one or more devices.
Regardless, evaluation subsystem 14 (e.g., evaluation device 14 in
FIG. 1) can communicate with acquisition subsystem 12 (e.g., one or
more of infrared devices 12A-B in FIG. 1), sensing subsystem 18
(e.g., one or more of sensing devices 18A-B in FIG. 1), and/or
identification subsystem 16 (e.g., identification device 16 in FIG.
1). In an embodiment, sensing subsystem 18 sends a notification to
evaluation subsystem 14 when a vehicle 2 is detected, and
evaluation subsystem 14 sends a notification to acquisition
subsystem 12 and/or identification subsystem 16 to instruct
subsystems 12, 16 to capture data on vehicle 2. Further, sensing
subsystem 18 can send the notification directly to acquisition
subsystem 12 and/or identification subsystem 16, as
illustrated.
In any event, evaluation subsystem 14 is shown implemented as a
computing device 20 that comprises an evaluation program 30, which
makes computing device 20 operable to evaluate vehicle(s) 2 (FIG.
1) by performing the process described herein. Computing device 20
is shown including a processing component 22 (e.g., one or more
processors), a storage component 24 (e.g., a storage hierarchy), an
input/output (I/O) component 26 (e.g., one or more I/O interfaces
and/or devices), and a communications pathway 28. In general,
processing component 22 executes program code, such as evaluation
program 30, which is at least partially stored in storage component
24. While executing program code, processing component 22 can read
and/or write data to/from storage component 24 and/or I/O component
26. Pathway 28 provides a communications link between each of the
components in computing device 20, while I/O component 26 provides
a communications link between a user and computing device 20. To
this extent, I/O component 26 can comprise one or more human I/O
devices, which enable a human user to interact with computing
device 20 and/or one or more communications devices to enable a
system user, e.g., inspection system 40, to communicate with
computing device 20 using any type of communications link.
Regardless, computing device 20 can comprise any general purpose
computing article of manufacture capable of executing program code
installed thereon. However, it is understood that computing device
20 and evaluation program 30 are only representative of various
possible equivalent computing devices that may perform the process
described herein. To this extent, in other embodiments, the
functionality provided by computing device 20 and evaluation
program 30 can be implemented by a computing article of manufacture
that includes any combination of general and/or specific purpose
hardware and/or program code. In each embodiment, the program code
and hardware can be created using standard programming and
engineering techniques, respectively.
Similarly, computer system 11 is only illustrative of various types
of computer systems for implementing aspects of the invention. For
example, in one embodiment, evaluation subsystem 14 comprises two
or more computing devices that communicate over any type of
communications link, such as a network, a shared memory, or the
like, to perform the process described herein. Further, while
performing the process described herein, one or more computing
devices in computer system 11 can communicate with one or more
other computing devices external to computer system 11 using any
type of communications link. In any event, a communications link
can comprise any combination of various types of wired and/or
wireless links; comprise any combination of one or more types of
networks; and/or utilize any combination of various types of
transmission techniques and protocols.
As discussed herein, evaluation program 30 enables computing device
20 to evaluate a vehicle 2 (FIG. 1). To this extent, evaluation
program 30 is shown including a vehicle module 32, a processing
module 34, an analysis module 36, and a forwarding module 38.
Operation of each of these modules is discussed further herein.
However, it is understood that some of the various modules shown in
FIG. 1 can be implemented independently, combined, and/or stored in
memory of one or more separate computing devices that are included
in computer system 11. Further, it is understood that some of the
modules and/or functionality may not be implemented, or additional
modules and/or functionality may be included as part of computer
system 11. Still further, it is understood that the various
subsystems 12, 14, 16, 18 can be implemented on any combination of
one or more computing devices.
Regardless, aspects of the invention provide a solution for
evaluating a vehicle, e.g., as part of a pre-inspection for a
vehicle inspection location. FIG. 3 shows an illustrative process
for evaluating a vehicle according to an embodiment, which can be
implemented by computer system 11 (FIG. 2). Referring to FIGS. 1-3,
in process P1, sensing subsystem 18 can automatically detect a
presence of a vehicle 2. Sensing subsystem 18 can comprise any type
of system capable of detecting a presence of vehicle 2 using any
solution. For example, sensing subsystem 18 can comprise an
electric eye, which includes a light sensor and/or light source
(e.g., sensing devices 18A-B), a magnetic sensor, and/or the
like.
In process P2, computer system 11 acquires evaluation data 50 for
vehicle 2. Evaluation data 50 can include vehicle identification
data 52 and infrared data 54. To this extent, identification
subsystem 16 can obtain vehicle identification data 52 for
distinguishing vehicle 2 from another vehicle, and acquisition
subsystem 12 can obtain infrared data 54 for vehicle 2. In an
embodiment, process P2 is performed automatically in response to a
presence of vehicle 2 being detected in process P1. To this extent,
identification subsystem 16 and acquisition subsystem 12 can be
located in close proximity to sensing subsystem 18 so that a
location and/or speed of vehicle 2 is known within a required
accuracy. Further, computer system 11 can be located such that only
vehicles 2 to be inspected are likely to be traveling and be
detected. Still further, computer system 11 can be located such
that it is highly probable that each vehicle 2 has recently applied
its brakes, for example, at a rest area, a weigh station adjacent
to a highway, at a bottom of a hill, and/or the like.
Identification subsystem 16 can acquire vehicle identification data
52 using any solution. To this extent, vehicle identification data
52 can comprise any type of data for distinguishing vehicle 2 from
other vehicle(s) being evaluated. For example, identification
subsystem 16 can include a radio frequency identification (RFID)
tag reader, which obtains vehicle identification data 52 from an
RFID tag on vehicle 2. Further, identification subsystem 16 can
include an image/video-based identification system, which can
obtain at least one visible image of vehicle 2. Still further,
vehicle identification data 52 can include other data, such as a
date/time stamp, a unique identifier (e.g., a serial number, a
count), and/or the like. To this extent, vehicle module 32 can
assign a unique identifier for the data for vehicle 2 upon its
detection, which is subsequently provided to and used by other
subsystems in computer system 11 to manage the corresponding
evaluation data 50 and track vehicle 2 as it is evaluated and/or
inspected.
Acquisition subsystem 12 can obtain infrared data 54 using any
solution. For example, acquisition subsystem 12 can comprise a
plurality of infrared-based imaging devices, which are located on
multiple sides of vehicle 2. Each infrared-based imaging device can
acquire a set of infrared images for a corresponding side of
vehicle 2. In an embodiment, acquisition subsystem 12 includes two
infrared devices 12A-B as shown in FIG. 1, each of which can
acquire a set of infrared images as vehicle 2 passes through the
corresponding fields of view. It is understood that the number,
configuration, and fields of view of infrared devices 12A-B is only
illustrative, and any number of infrared devices 12A-B can be used
to obtain infrared image(s) for any side of vehicle 2, including
the front, back, top, bottom, etc. Additionally, it is understood
that an infrared image may include only a portion of vehicle 2.
Evaluation data 50 can include additional types of data, which can
be acquired by acquisition subsystem 12 and/or another subsystem
(not shown). For example, evaluation data 50 can include image data
based on visible light, ultraviolet light, and/or the like and/or
non-image data, such as radar data, X-ray data, radiation data,
magnetic data, pressure data, spectrometric data, acoustic data, a
weight of vehicle 2, and/or the like. To this extent, acquisition
subsystem 12 can obtain any combination of various types of
evaluation data 50 using any solution. For example, acquisition
subsystem 12 can include a microphone array to acquire acoustic
data for vehicle 2. Similarly, acquisition subsystem 12 can include
contact-based (e.g., pressure) and/or non-contact-based (e.g.,
laser/diffuse light) sensor(s) for registering each wheel that
passes for a vehicle 2. Still further, acquisition subsystem 12 can
include a set of wireless receivers that can detect signals from
sensor(s) or system(s), such as SAW-based RF tags, implemented on
vehicle 2, and which monitor one or more operating characteristics
of vehicle 2 (e.g., tire pressure, engine data, and/or the
like).
In process P3, computer system 11 pre-processes evaluation data 50,
such as vehicle identification data 52 and/or infrared data 54. For
example, when identification subsystem 16 obtains a set of visible
images for vehicle 2, identification subsystem 16 can pre-process
the visible image(s) to extract, enhance, isolate, and/or the like,
vehicle identification data 52 such as an image of a license plate,
operating credentials (e.g., located on a side of the vehicle),
and/or the like. Further, identification subsystem 16 can obtain
vehicle identification data 52 that may not uniquely identify
vehicle 2. For example, identification subsystem 16 can obtain a
color of vehicle 2, a size/shape/type of vehicle 2 (e.g., truck
tractor and/or trailer(s)), and/or the like. To this extent,
identification subsystem 16 can ensure that vehicle 2 is a proper
type of vehicle that is being inspected (e.g., not a passenger
vehicle). Regardless, identification subsystem 16 can include the
raw data (e.g., image(s)/video of vehicle 2) from which one or more
identifying attributes of vehicle 2 is/are derived as vehicle
identification data 52.
Additionally, acquisition subsystem 12 can pre-process some or all
of evaluation data 50, such as infrared data 54. To this extent,
each infrared device 12A-B can perform filtering, initial
processing, and/or the like, on the infrared data using any
solution. For example, infrared data corresponding to a critical
portion of the image can be extracted. Additionally, an infrared
image can be filtered to reduce incidental noise, glare, and/or the
like. Further, an infrared device 12A-B can eliminate shadows,
infrared or otherwise, from an image, e.g., using reflective
symmetry detection, common movement, movement conforming to
ground/field objects, and/or the like. Still further, when
evaluation data 50 includes other types of data, acquisition
subsystem 12 can perform noise reduction, signal amplification and
smoothing, and/or the like, on evaluation data 50. The initial
processing of evaluation data 50 also can include securing the data
(e.g., watermarking, encrypting, and/or the like), compressing the
data, and/or the like. In any event, acquisition subsystem 12 can
store the pre-processed evaluation data 50 and/or the raw
evaluation data 50 for each vehicle 2.
When computer system 11 includes multiple devices as illustrated in
FIG. 1, in process P4, the devices that acquire evaluation data 50
can transmit the data for use on evaluation subsystem 14. For
example, each infrared device 12A-B and identification device 16
can transmit infrared data 54 and vehicle identification data 52,
respectively, for use on evaluation subsystem 14 using any
solution. It is understood that the transmission can incorporate
data security, data compression, and/or other transmission
techniques known in the art. In an embodiment, the data is
transmitted using a wireless communications solution. In this
manner, computer system 11 can be readily set up on a temporary or
permanent basis. Regardless, in process P5, evaluation subsystem 14
can receive evaluation data 50 from subsystems 12, 14 using any
solution. Alternatively, one or more subsystems could be
implemented on a single device, in which case processes P4-P5 may
not be required.
In any event, in process P6, processing module 34 can process some
or all of evaluation data 50. To this extent, processing module 32
can process image-based identification data 52 to extract one or
more identifying features of vehicle 2 (e.g., a license plate
number, operating credentials, and/or the like). Additionally,
vehicle module 32 can match identification data, such as RFID tag
information, a license plate number, operating credentials, and/or
the like, with a known database of vehicles 2, e.g., in a
state/national database, private fleet, and/or the like. Further,
processing module 34 can perform advanced image processing on
infrared data 54 to enhance (e.g., define, extract, identify,
and/or the like) one or more signal features. For example,
processing module 34 can subject infrared data 54 to one or more of
segmentation, fusion between multiple images, feature detection,
and/or the like. Additionally, when evaluation data 50 includes
other types of data, processing module 34 can fuse different types
of data in evaluation data 50. By using data fusion, processing
module 34 can provide redundant evaluation data 50 that is more
susceptible for accurate detection of various phenomena, and is
less prone to false positive/negative readings.
In process P7, analysis module 36 can analyze evaluation data 50 to
determine whether any anomalies may be present. To this extent,
analysis module 36 can implement any combination of various
decision-making solutions including a trained neural network, an
expert system, template matching, a Markov Model, and/or the like.
These decision-making solutions can examine some or all of
evaluation data 50 to determine whether an anomaly is present. For
example, a set of infrared images can be examined to determine
whether one or more areas may exhibit heat that is outside of an
expected range, e.g., either too cold or too hot. Analysis module
36 can analyze evaluation data 50 for the presence of various types
of anomalies. Different types of evaluation data 50 may be more
useful for determining different types of anomalies. For example,
visible image data could be used to determine whether a leak, a
loose hose, or the like, may be present on the vehicle, while
ultraviolet image data could be used to identify the presence of
excess strain or the like. Further, analysis module 36 can obtain
anomaly information from one or more external sources. For example,
analysis module 36 can provide a license plate, operating
credentials, and/or the like, for comparison with a law enforcement
database, maintenance history database, and/or the like. In this
case, analysis module 36 can receive a response that indicates
whether an anomaly may be present due to operation of the vehicle
itself (e.g., stolen vehicle, suspended license, past due
maintenance, and/or the like).
In decision D1, analysis module 36 can determine whether one or
more anomalies are present. If so, in process P8, analysis module
36 can store anomaly data 56 for the vehicle. Anomaly data 56 can
include information on the anomaly(ies) located in evaluation data
50. Further, anomaly data 56 can include one or more recommended
actions as a result of the anomaly(ies). For example, anomaly data
56 can include a recommended type of inspection and/or area of
inspection. When computer system 11 is implemented as a preliminary
evaluation system (e.g., a pre-inspection system), in process P9,
forwarding module 38 can transmit some or all of evaluation data
50, including anomaly data 56, for use by a primary evaluation
system, such as inspection system 40, for temporary or permanent
storage, and/or the like.
It is understood that the process is only illustrative of various
processes that can be implemented. For example, forwarding module
38 can provide a result of the analysis together with vehicle
identification data 52 for use by a remote, primary evaluation
system, such as inspection system 40, for every vehicle, regardless
of whether any anomaly(ies) were detected. Further, vehicle module
32 can manage all evaluation data 50 for one or more vehicles using
any solution. To this extent, vehicle module 32 can store
evaluation data 50 using any solution (e.g., one or more files,
records in a database, and/or the like). Further, vehicle module 32
can manage an interface such as a user interface, application
program interface (API), and/or the like, which enables a user to
perform one or more operations on evaluation data 50. Still
further, vehicle module 32 can automatically perform maintenance,
such as purging evaluation data 50 that is no longer required,
using any solution.
As described herein, an embodiment of acquisition subsystem 12
utilizes acquisition devices, such as infrared devices 12A-B, which
are portable and can be readily deployed and/or removed. To this
extent, FIGS. 4A-C show an illustrative acquisition unit 60
according to an embodiment. As illustrated in FIGS. 4A-B,
acquisition unit 60 includes a power unit, which can include solar
panels 62A-B, a support structure, which can include foldable legs
64A-C, and a sensor head 66. It is understood that solar panels
62A-B are only an illustrative solution for generating power for
acquisition unit 60, and other solutions, including a power unit
that does not include independent power generation (e.g., only a
battery), can be utilized. Similarly, it is understood that
foldable legs 64A-C are only illustrative, and the support
structure can be implemented using any solution.
FIG. 4C shows a more detailed view of sensor head 66 according to
an embodiment. Sensor head 66 includes an acquisition bay 70, an
electronics bay 72, and an interface bay 74. Each bay 70, 72, 74
can include one or more components that implement various
functions. For example, acquisition bay 70 can include a set of
data acquisition devices, such as an infrared device 70A.
Electronics bay 72 can include a set of components for data
processing and storage, wireless communications, power supply and
distribution components, and/or the like. Additionally, interface
bay 74 can include one or more I/O interface ports (e.g., Ethernet,
USB, Firewire, and/or the like), one or more I/O interface devices
(e.g., display, keypad, and/or the like), a power interface (e.g.,
for a rechargeable battery), and/or the like. Further, sensor head
66 can include an antenna 76 for sending and/or receiving data via
a wireless communications system, and a mounting collar 78 for
permanently or temporarily mounting sensor head 66 to a permanent
or portable support structure.
Acquisition bay 70 can include any type of data acquisition
device(s) for acquiring a particular type of evaluation data 50
(FIG. 2). To this extent, infrared device 70A can comprise any type
of infrared imaging device. For example, infrared device 70A can
detect infrared radiation using an un-cooled microbolometer, an
un-cooled line-scan camera, lower-resolution infrared imaging
system, and/or the like. These types of infrared devices require
less power than devices that utilize a cooled infrared sensor,
which also can be implemented when sufficient power is not an issue
(e.g., a permanent emplacement). When a line-scan camera is
utilized, processing module 34 (FIG. 2) can combine the scanned
infrared data with information on the vehicle's speed to produce an
accurate scaled image from the successive slices scanned as the
vehicle passed. Additionally, infrared device 70A can comprise: a
near-infrared (NIR) imaging device, which is best when an anomaly
is indicated by a several hundred degrees temperature difference; a
medium-wave infrared (MWIR) imaging device, which can penetrate fog
and very humid air; or a long-wave infrared (LWIR) imaging device.
Moreover, infrared device 70A can comprise an imaging device that
combines a lower-resolution infrared image with a higher-resolution
visible light image to provide a fused infrared and visible
light-based image.
Further, infrared device 70A could comprise a fixed imaging unit or
a scanning sensor unit, such as a pan-tilt-zoom imaging device. In
the latter case, infrared device 70A can be controlled by
acquisition subsystem 12 (FIG. 2) and can scan key locations on
vehicle 2 (FIG. 1). As a result, infrared device 70A can acquire
zoomed, higher resolution images of the key locations for
subsequent analysis by the remainder of computer system 11 (FIG.
2). Additionally, with a zoom capability, infrared device 70A can
be placed at a greater distance from vehicle 2 and still obtain
high resolution image data for vehicle 2.
An image acquired by infrared device 70A can be blurred when the
image is captured before the microbolometers deplete their existing
charge from acquiring a previous image. Infrared device 70A and/or
electronics bay 72 can include one or more components to address
this problem using any solution. For example, infrared device 70A
can include an external physical shutter that shuts down the frame
for a sufficient period of time to clear the infrared device 70A.
Further, infrared device 70A can incorporate a wide field of view
to avoid a large shift in the image from frame to frame. Still
further, electronics bay 72 can process the image(s) using an image
deblurring algorithm, or the like. In this case, information on the
speed of vehicle 2 (FIG. 1) may be obtained (e.g., using a radar,
visible-light movement analysis, and/or the like) and utilized by
the algorithm. Still further, infrared device 70A can incorporate a
lower time constant microbolometer, a cooled imager, and/or the
like.
Infrared device 70A and/or electronics bay 72 also can incorporate
one or more features for helping to ensure accurate infrared
images. For example, infrared device 70A and/or electronics bay 72
can compensate for infrared drift offset. In particular, during
use, the response to infrared radiation of pixels in infrared
device 70A can shift slightly in unpredictable patterns. As a
result, infrared device 70A can include a shutter-based
recalibration, in which a shutter closes off the camera and the
pixel offsets can be recalibrated to a temperature of the shutter.
Frequently, the shutter-based recalibration is triggered
periodically. In an embodiment, infrared device 70A and/or
electronics bay 72 can trigger recalibration to occur frequently
when a vehicle is not present so that infrared device 70A will have
been recently recalibrated when a new vehicle is present.
Additionally, infrared device 70A can include a "cold shield" that
surrounds the infrared sensors and prevents any infrared
interference resulting from heat radiating from one or more
components in acquisition unit 60.
Sensor head 66 can be designed to operate in various weather
conditions and withstand frequent movement. In particular, sensor
head 66 can include a rugged construction, and include various
solutions for protecting the operating components from rain, snow,
and/or the like. Moreover, sensor head 66 can include various
ancillary systems to ensure proper operation of the various
components therein. To this extent, sensor head 66 can include
environmental/system monitoring components, self-cleaning
components, and/or the like.
Regardless, it is understood that sensor head 66 is only
illustrative. For example, sensor head 66 could be implemented as a
handheld device, which can be pointed at a vehicle 2 (FIG. 1) and
acquire and process the relevant evaluation data 50 (FIG. 2).
Further, in another embodiment, sensor head 66 and/or acquisition
unit 60 includes some or all of the components for sensing
subsystem 18 (FIG. 2) and/or identification subsystem 16 (FIG. 2).
To this extent, sensing subsystem 18 could comprise a "radar gun",
which can detect vehicle 2 at a sufficient range to prepare the
other components for operation. Additionally, identification
subsystem 16 could comprise a visible imaging device or the like,
and the image(s) can be utilized by both identification subsystem
18 and acquisition subsystem 12.
Returning to FIGS. 1 and 2, acquisition subsystem 12 may include
two or more acquisition units 60 (FIG. 4A) that are located on a
plurality of sides of vehicle 2. For example, each infrared device
12A-B can be located on a corresponding acquisition unit 60.
Similarly, sensing subsystem 18 and/or identification subsystem 16
can include one or more units that are configured similarly to
acquisition unit 60, but include the appropriate sensing devices
and processing capabilities to implement the corresponding
functions for the respective subsystems 16, 18.
However, this is only illustrative, and acquisition subsystem 12,
identification subsystem 16, and/or sensing subsystem 18 can
include an acquisition unit in an alternative location. For
example, FIGS. 5A-C show another illustrative acquisition unit 80
according to an embodiment. In particular, as shown in FIG. 5A,
acquisition unit 80 is located in a path of vehicle 2 such that
vehicle 2 will pass over acquisition unit 80. In this case,
acquisition unit 80 can acquire evaluation data 50 (FIG. 2) from an
interior side of vehicle 2. To this extent, acquisition unit 80 can
acquire evaluation data 50 for an interior side of one or more
wheels 4 (as shown). Further, acquisition unit 80 can acquire
evaluation data 50 for an undercarriage of vehicle 2. Still
further, as vehicle 2 is approaching and/or leaving the location of
acquisition unit 80, acquisition unit 80 can acquire evaluation
data 50 for a front and/or back of vehicle 2. Acquisition unit 80
can be permanently or temporarily placed at a location using any
solution.
Acquisition unit 80 can include similar components as shown and
described with respect to sensor head 66 (FIG. 4C). To this extent,
acquisition unit 80 can include an imaging device, such as an
infrared device and/or visible imaging device, for each side of
vehicle 2 to be imaged, data processing, storage, interface, and
communications components, a power source, and/or the like. In an
embodiment, acquisition unit 80 includes a single imaging device
for a plurality of sides of vehicle 2 to be imaged. For example, as
shown in FIGS. 5B-C, acquisition unit 80 can include a single
imaging device 82 (e.g., an infrared device, shown only in FIG. 5B
for clarity) that images light (e.g., infrared light) that passes
through both windows/lenses 84A-B. The light is then reflected from
mirrors 86A-B onto a concave mirror 88 and then a convex mirror 90,
which directs the light through a transparent portion 88A of
concave mirror 88 and onto an imaging sensor of imaging device
82.
Acquisition unit 80 can include electronic shutters 92A-B (shown
only in FIG. 5B for clarity) that alternatively block light passing
through one of windows/lenses 84A-B to enable a clear acquisition
of both fields of view. Electronic shutters 92A-B can operate at a
speed commensurate with a frame rate of imaging device 82, thereby
enabling each field of view to be imaged every nth frame (where n
is the number of fields of view, two in this embodiment).
Alternatively, imaging device 82 could image each field of view in
a unique subdivision of its imaging area, thereby enabling
simultaneous imaging of all fields of view.
FIG. 6 shows an illustrative configuration of acquisition units
94A-C according to an embodiment. In this case, acquisition units
94A-C are configured for use in a high speed environment (e.g., a
highway). As illustrated, acquisition units 94A-C are mounted to
supports for road signage or the like. This can enable acquisition
units 94A-C to acquire infrared data for vehicles 2 from various
angles. Further, road 96 and/or other surfaces can include
reflective material that can direct infrared data towards one or
more acquisition units, such as acquisition unit 94A. Similarly,
acquisition units 94A-C could be placed at other, lower speed
locations, such as toll booths, or the like.
Returning to FIG. 2, depending on the type(s) of evaluation data 50
acquired and processed, analysis module 36 can detect any
combination of various types of anomalies, and the corresponding
flaws, that may be present on a vehicle 2 (FIG. 1). For example,
analysis module 36 can detect acoustic anomalies, which can be used
to identify a flawed (e.g., worn) bearing, malfunctioning engine,
and/or the like. Further, some evaluation data 50 may identify an
anomaly directly, such as a sufficiently high measurement of
radiation data.
As discussed herein, analysis module 36 can use infrared data 54,
such as one or more infrared images of vehicle 2, alone and/or in
conjunction with other types of data to determine the presence of
any anomalies in vehicle 2. To this extent, infrared data 54 that
includes one or more areas that are hotter than normal can be used
to detect defects such as: stuck brake, under-inflated/flat tire,
leaking exhaust (heat in abnormal area), worn bearings, overheating
engine compartment, cracked frame (causing additional flexing and
therefore additional heat), overheating radiator, overheating cargo
(e.g., chemicals or biologicals which may react with each other to
create additional heat or even catch on fire--compost, rags filled
with oil and other reactive solvents, sodium, etc.), and/or the
like. Similarly, infrared data 54 that includes one or more areas
that are cooler than normal can detect defects such as: failing
brakes, non-operating brakes, loss of cooling in refrigerated area,
and/or the like. Still further, analysis module 36 can process
infrared data 54 to measure one or more attributes of a portion of
vehicle 2, which analysis module 36 then uses to determine the
presence of a defect. For example, analysis module 36 can thermally
map tread depth for a wheel 4 (FIG. 5A) of vehicle 2 using infrared
data 54, which analysis module 36 can compare to a standard to
determine whether the tread depth is sufficient. Further, analysis
module 36 can use infrared data 54 to identify unexpected voids
within a cargo area (which would affect heat transmission through
vehicle 2), which can then be flagged for a follow up
inspection.
Additional illustrative details are described with reference to the
use of infrared image(s) to detect one or more anomalies with
respect to the brakes, bearings, and/or wheels of a vehicle. To
this extent, FIG. 7 shows an illustrative vehicle 2 and vehicle
wheel 4, which can be evaluated according to an embodiment. In
general, wheel 4 includes a wheel rim 6 and a tire 8. Wheel rim 6
is attached to a central axle 6A and includes a bearing area 6B
(the bearings are internal in this area) and a plurality of holes,
such as hole 6C, through which a brake drum can be viewed. Tire 8
is affixed to wheel rim 6 and contacts the road along a tread
surface 8A.
When the brakes of vehicle 2 are applied, friction occurs at the
brake drum and the corresponding heat dissipates through holes 6C.
Similarly, a worn bearing will cause additional heat in bearing
area 6B than that seen for a properly operating bearing. Further,
abnormal heating of tread surface 8A will occur due to
under-inflation, tread separation, and/or the like. In each case,
analysis module 36 (FIG. 2) can examine infrared data 54 (FIG. 2)
that includes a set of infrared images, each of which includes some
or all of wheel 2 to determine the presence of one or more
anomalies. In particular, analysis module 36 can examine the
relevant portion(s) of wheel rim 6 and tread surface 8A to
determine whether any brake, wheel, and/or bearing-related
anomoly(ies) is/are present on vehicle 2.
Analysis module 36 (FIG. 2) can process infrared image(s) of wheel
4 using any combination of one or more image processing algorithms.
For example, FIG. 8 shows an illustrative series of images 100A-C
of a wheel and the resulting infrared data after processing with
two illustrative evaluation solutions according to an embodiment.
In particular, analysis module 36 can generate infrared data 102A-C
by processing the corresponding images 100A-C using an edge
detection algorithm. The edge detection algorithm detects edges in
an image by analyzing local brightness changes over short
distances. Similarly, analysis module 36 can generate infrared data
104A-C by processing the corresponding images 100A-C using a
thresholding algorithm. The thresholding algorithm assigns each
pixel to white or black, depending on whether a brightness level of
the pixel exceeds a threshold brightness level.
Image 100A corresponds to a wheel 4 (FIG. 7) having cold brakes,
image 100B corresponds to a wheel 4 having warm brakes, and image
100C corresponds to a wheel 4 having hot bearings. As can be seen,
the infrared data 102A-C generated by applying an edge detection
algorithm yields a clear distinction between cold brakes infrared
data 102A and warm brakes infrared data 102B. However, only a
minimal difference is present between warm bearings infrared data
102C and the normal bearings of infrared data 102A-B. As a result,
the edge detection algorithm may not efficiently detect warm
bearings. Additionally, the infrared data 104A-C generated by
applying a thresholding algorithm can be used to readily
distinguish between both cold brakes (infrared data 104A) and warm
brakes (infrared data 104B) and hot bearings (infrared data 104C)
and normal bearings (infrared data 104A-B).
It is understood that each algorithm can be calibrated to
successfully evaluate infrared images 100A-C. For example, the
thresholding algorithm can be calibrated so that the threshold
brightness level is set to an expected brightness, which is based
on a level of brightness that corresponds to proper braking and/or
bearing operation. The thresholding algorithm can be executed
multiple times, each with an expected brightness that corresponds
to an anomaly. For example, a first expected brightness may be set
to a highest acceptable brightness, above which the brake is
labeled as "hot"; a second expected brightness may be set to a
lowest acceptable brightness, below which the brake is labeled as
"cold"; and a third expected brightness may be set to a highest
acceptable brightness, above which the bearings are labeled as
"hot". After each application of the thresholding algorithm, the
resulting infrared data can be analyzed using any solution. The
expected brightness may be adjusted based on one or more factors,
such as ambient conditions, a weight of the vehicle, a typical
amount of recent braking, and/or the like.
Returning to FIG. 2, it is understood that the edge detection and
thresholding algorithms are only illustrative of various types
and/or combinations of algorithms that analysis module 36 can
implement. For example, with proper calibration, analysis module 36
can perform a thermal mapping of infrared images 100A-C (FIG. 8)
and compare the actual heat values with expected values across
different areas of a wheel 4 (FIG. 7) and/or vehicle 2 (FIG. 7).
Further, analysis module 36 can process an infrared image of tread
surface 8A (FIG. 7) to measure a depth of the tread, in which a
temperature difference between high and low points depends on a
thickness of the tread (e.g., a thicker tread will have a greater
difference than a thinner tread). Still further, analysis module 36
can implement a curve-fitting algorithm to match edge features
detected by the edge detection algorithm to a circle matching an
expected area for detecting a brake cylinder. Still further,
analysis module 36 can implement algorithms such as contrast
enhancement, image histogram adjustment, blob and object detection,
and/or the like. Additionally, evaluation data 50 can include other
types of data for which analysis module 36 can implement similar
algorithms for detecting relevant signal features in the data and
discriminating between the absence or presence of one or more
anomalies.
In an embodiment, acquisition subsystem 12 detects a set of
infrared signatures for vehicle 2 (FIG. 1). For example,
acquisition subsystem 12 can include a linear array of infrared
sensing devices that capture infrared data. FIG. 9 shows an
illustrative use of a linear array 110 according to an embodiment.
In general, vehicle 2 can pass linear array 110, which includes a
plurality of infrared-sensing elements at varying heights. The data
obtained by the infrared-sensing elements can be used to produce a
set of infrared signatures 112 for vehicle 2 (e.g., by averaging or
otherwise combining all the data, generating infrared signatures
for multiple heights--wheel centerline, undercarriage height,
etc.). The infrared signature(s) 112 can be normalized against an
ambient temperature plot 114 can compared to an expected signature
116. A significant variation (e.g., based on a max/min temperature
in infrared signature 112, a goodness of fit to expected signature
116, and/or the like) of infrared signature 112 from the expected
signature 116 can identify a potential anomaly and/or be flagged
for further inspection. For example, the variation shown in FIG. 9
may indicate that middle wheel of vehicle 2 is overheating.
Further, it is understood that linear array 110 can be used to
acquire image(s) of vehicle 2 by, for example, passively scanning
vehicle 2 (e.g., imaging vehicle 2 in discrete sections as it
passes), actively scanning vehicle 2 (e.g., using a mechanism to
cause a field of view of linear array 110 to sweep a length of
vehicle 2), and/or the like. These images can be used in the same
manner as discussed herein.
The management (e.g., selection, calibration, utilization, etc.) of
a set of algorithms for evaluating a vehicle 2 (FIG. 7) can be
implemented using any type of artificial intelligence solution. For
example, analysis module 36 can implement an expert system that
comprises a set of condition examinations and corresponding action
events, to evaluate vehicle 2. Such an expert system could include
a condition examination such as a number of white pixels in
infrared data 104A-C (FIG. 8), and perform some action (e.g.,
further analyze evaluation data 50 for hot/cold brakes, worn
bearings, and/or the like) if the number is below/above an expected
range. It is understood that the expert system can implement fuzzy
logic (e.g., assign probabilities) to arrive at a conclusion with
respect to the presence/absence of an anomaly in vehicle 2.
Further, analysis module 36 can implement a neural network, which
includes a series of connected neural units, each of which triggers
upon a certain set of conditions. The neural network can be trained
using sample data and include an ability to self-modify the
characteristics of the units in the neural network using
backpropagation or the like. Still further, analysis module 36 can
implement template or pattern matching, in which evaluation data 50
is compared to a set of templates or patterns that have particular
characteristics of interest. In this case, a proper tolerance for
assigning matches is critical so that evaluation data 50 is not
under or over matched with the corresponding templates or
patterns.
As discussed herein, evaluation data 50 can include multiple types
of data on vehicle 2 (FIG. 1), which can be obtained at varying
locations and/or times, and which have been fused by processing
module 34. In this case, analysis module 36 can compare features
detected in the different types of data, and eliminate some
potential confounding variables that can yield a false
positive/negative. For example, holes 6C (FIG. 7) may be visible in
a visible image and only visible in infrared data if an anomaly is
present. When visible in the infrared data, analysis module 36 can
overlay the locations with the visible image to verify a location
of the hot spots. Additionally, acoustic data can be used to
determine whether a sound typically associated with a failing
bearing is present when a bearing-related anomaly is indicated by
infrared data. Still further, visible light and infrared-based
profiles of vehicle 2 can be compared to determine the location of
infrared heat signatures as compared with known vehicular
systems.
To assist in calibrating computer system 11, an infrared
calibration fixture can be obtained, which includes a thermal grid
pattern tailored for use in calibrating computer system 11 using
any solution. An infrared device, such as infrared devices 12A-B
(FIG. 1), can include an automatic internal shutter that provides
relative zeroing capability. For example, the internal shutter can
provide an imager with an effectively even temperature at all
points for calibration between pixels. To this extent, infrared
arrays can include pixels of different specific reactivity,
sensitivity, and gain, which must be calibrated to produce an
accurate and even image.
Additionally, it may be desirable to determine an approximate
temperature of objects/features detected in an infrared image. To
this extent, a target (such as the infrared calibration fixture)
having areas of known temperatures (within a tolerance) can be
placed in the field of view of an infrared device 12A-B (FIG. 1)
during imaging of vehicle 2 (FIG. 1). In this case, the imaged
target can be compared with the infrared image of vehicle 2 to
determine a temperature corresponding to the objects/features
detected on vehicle 2. The target can be constructed using
heating/cooling elements and thermostatic elements that ensure that
the temperature of the components is maintained to within
relatively small tolerances. Additionally, the target can be
customized to fit appropriate infrared emissivity profiles of one
or more components, e.g., of braking components, thereby providing
similar patterns that would be seen from the corresponding
component(s).
Further, the fields of view for two or more infrared devices 12A-B
can be registered with one another so that the locations of common
elements can be accurately determined. A band-pass filter or the
like can be used to adjust a temperature sensing range for of one
or more of the infrared devices 12A-B to a slightly different band.
Differences in the images captured by the infrared devices 12A-B
would then be due to differences in the heat radiation emitted.
These differences can be used to generate a temperature map for
vehicle 2. Alternatively, a single infrared device 12A-B with
switchable and/or tunable filters may be used.
An inherent temperature sensing range for many infrared devices
12A-B may be narrower than that of a range of temperatures of
potential interest. For example, many commercial infrared devices
12A-B "top out" at around 500 degrees Fahrenheit, while exhaust
gases, highly heated components of exhaust systems, failing
brakes/bearing, and/or the like, may have temperatures around 1,000
degrees Fahrenheit. To this extent, acquisition subsystem 12 can
include one or more components to detect temperature variations
across wider ranges of temperatures. In an embodiment, acquisition
subsystem 12 uses a neutral-density filter to effectively reduce
the radiation by a factor (e.g., 3:1, 10:1, and/or the like) across
the sensitive band of infrared devices 12A-B. In this case,
saturation will not occur until a much higher temperature, but some
sensitivity will be lost. For example, using a factor of 3:1 will
result in a 1500 degree span being imaged across a 500 degree
span.
Additionally, acquisition subsystem 12 can fuse multiple captured
frames having different characteristics (e.g., longer exposure,
different filters, and/or the like) into a higher-bit resolution
digital image that maintains the temperature information in each
separate frame. For example, three frames, each of which has a 300
degree sensitive range in three adjacent ranges (e.g., 0-300,
300-600, 600-900) can be utilized. The frames can be superimposed
with sufficient color and brightness bit resolution to discriminate
clearly between all three conditions throughout the fused frame.
When performed on a moving target, such as vehicle 2 (FIG. 1),
acquisition subsystem 12 must register the images across the
frames, compensate for the movement, potential blurring, and/or the
like, and combine the images into a single registered composite
image for analysis.
Computer system 11 is described herein as performing a preliminary
evaluation of vehicles, and providing some or all of evaluation
data 50 for use by an inspection system 40. A particular embodiment
would include implementation of computer system 11 as part of a
commercial vehicle inspection station (e.g., weigh station). In
this manner, individuals using the inspection system 40 can use the
data to perform a more focused inspection of a particular vehicle
and/or set of vehicles, while allowing vehicles without any
detected anomalies to proceed more quickly through the inspection.
As a result, inspection system 40 can more efficiently inspect
vehicles while removing a higher percentage of unsafe vehicles from
operation.
However, it is understood that this embodiment is only
illustrative. For example, computer system 11 can be implemented as
part of a fleet management system for a fleet of vehicles, such as
commercial vehicles or buses. In this case, computer system 11 can
obtain a historical record of previous inspection(s) and can permit
condition-based rather than schedule-based maintenance on the
vehicles. As a result, a fleet owner will only need to replace
parts that are actually out of a given tolerance range, saving the
expense of replacing parts too early. Such an embodiment can
evaluate vehicles as they arrive and/or depart to/from a
destination (e.g., a warehouse). Additionally, a third party
maintenance company could charge the fleet owner only for those
vehicles/components that are found out of tolerance. Further,
computer system 11 can be integrated into other types of
operations, such as security applications, manufacturer databases,
governmental regulation compliance systems, and/or the like.
Further, while aspects of the invention have been shown and
described with respect to the use of infrared data with or without
other types of data in evaluating a vehicle, it is understood that
alternative embodiments may be implemented without the use of
infrared data. To this extent, embodiments may obtain evaluation
data that includes image data based on visible light, ultraviolet
light, and/or the like and/or non-image data, such as radar data,
X-ray data, radiation data, magnetic data, pressure data,
spectrometric data, acoustic data, a weight, and/or the like. The
particular combination of types of data can be varied based on a
particular application of the embodiment. For example, an
embodiment can obtain ultraviolet light-based image data that is
used to evaluate a presence of an unacceptable amount of strain for
one or more parts of the vehicle. Additionally, an embodiment can
obtain acoustic data, which can be evaluated to determine engine
performance, bearing performance, and/or the like.
While shown and described herein as a method and system for
evaluating a vehicle, it is understood that the invention further
provides various alternative embodiments. For example, in one
embodiment, the invention provides a computer program stored on a
computer-readable medium, which when executed, enables a computer
system to evaluate a vehicle. To this extent, the computer-readable
medium includes program code, such as evaluation program 30 (FIG.
2), which implements the process described herein. It is understood
that the term "computer-readable medium" comprises one or more of
any type of tangible medium of expression capable of embodying a
copy of the program code (e.g., a physical embodiment). In
particular, the computer-readable medium can comprise program code
embodied on one or more portable storage articles of manufacture,
on one or more data storage portions of a computing device, such as
storage component 24 (FIG. 2), as a data signal traveling over a
network (e.g., during a wired/wireless electronic distribution of
the computer program), on paper (e.g., capable of being scanned and
converted to electronic data), and/or the like.
In another embodiment, the invention provides a method of
generating a system for evaluating a vehicle. In this case, a
computer system, such as computer system 11 (FIG. 1), can be
obtained (e.g., created, maintained, having made available to,
etc.) and one or more programs/systems for performing the process
described herein can be obtained (e.g., created, purchased, used,
modified, etc.) and deployed to the computer system. To this
extent, the deployment can comprise one or more of: (1) installing
program code on a computing device, such as computing device 20
(FIG. 2), from a computer-readable medium; (2) adding one or more
computing devices to the computer system; and (3) incorporating
and/or modifying one or more existing devices of the computer
system, to enable the computer system to perform the process
described herein.
In still another embodiment, the invention provides a business
method that performs the process described herein on a
subscription, advertising, and/or fee basis. That is, a service
provider could offer to evaluate one or more vehicles as described
herein. In this case, the service provider can manage (e.g.,
create, maintain, support, etc.) a computer system, such as
computer system 11 (FIG. 1), that performs the process described
herein for one or more customers. In return, the service provider
can receive payment from the customer(s) under a subscription
and/or fee agreement, receive payment from the sale of advertising
to one or more third parties, and/or the like.
As used herein, it is understood that "program code" means any set
of statements or instructions, in any language, code or notation,
that cause a computing device having an information processing
capability to perform a particular function either directly or
after any combination of the following: (a) conversion to another
language, code or notation; (b) reproduction in a different
material form; and/or (c) decompression. To this extent, program
code can be embodied as any combination of one or more types of
computer programs, such as an application/software program,
component software/a library of functions, an operating system, a
basic I/O system/driver for a particular computing, storage and/or
I/O device, and the like.
The foregoing description of various aspects of the invention has
been presented for purposes of illustration and description. It is
not intended to be exhaustive or to limit the invention to the
precise form disclosed, and obviously, many modifications and
variations are possible. Such modifications and variations that may
be apparent to an individual in the art are included within the
scope of the invention as defined by the accompanying claims.
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